MENTAL HEALTH DIAGNOSES AND ACUTE CARE USE AMONG INDIVIDUALS WITH SYSTEMIC LUPUS ERYTHEMATOSUS IN THE ALL OF US RESEARCH PROGRAM
Notice bibliographique
Résumé
PV080 / #137 Poster Topic: AS11 - Epidemiology and Public Health Background/Purpose Mental health disorders, including depression, anxiety, and post-traumatic stress disorder, are prevalent among people living with SLE. We hypothesized that these mental health conditions may increase recurrent acute care use (emergency department [ED] visits, hospitalizations) among patients with SLE. Methods We used data from the nationwide All of Us Research Program (version 7), an NIH cohort of >287,000 U.S. adults who enrolled and consented for linkage to their electronic health records. We identified those with ≥ 2 ICD-10 or SNOMED codes for SLE in the ≤ 2 years preenrollment. We assessed the exposure of concomitant mental health diagnoses during that time, identified by ≥ 2 ICD-10 or SNOMED codes for major depression, anxiety, or PTSD (mental health diagnoses) in the ≤ 2 years preenrollment. The outcome was number of emergency department visits (only) and hospitalizations (including those from emergency department) after All of Us enrollment date. We used multivariable negative binomial regression models to examine associations between having a mental health diagnosis and acute care use. Models were adjusted for age, sex, race, ethnicity, calendar year of enrollment, other baseline period sociodemographic factors and comorbidities. Results We identified 1,683 with SLE, among whom 1,029 (61.1%) had depression, anxiety, and/or PTSD. Mean age overall was 49.45 (14.26) and 89.4% were female. (Table 1) Those with diagnoses of ≥ 1 mental health condition were more likely to be less educated, in a low-income group, to have ever smoked, and to have more baseline comorbidities. Patients were followed for a mean of 29.9 months (SD 15.3) after enrollment. In adjusted analyses (Table 2), we found associations between having mental health diagnoses and higher rates of acute care use, both emergency department visits (adjusted IRR 1.84, 95% CI 1.52-2.22) and hospitalizations (IRR 1.40, 95% CI 1.11-1.78). This was true both for all emergency visits and hospitalizations for SLE (IRR 1.61, 95% CI 1.32-1.97) and for all diagnoses (IRR 1.45, 95% CI 1.19, 1.78). Table 1. Characteristics of the Patients with SLE in the All of Us Research Program (version 7) by presence of Concomitant Mental Health Diagnoses Table 2. Incidence Rate Ratios for Acute Care Use for Patients with Mental Health Conditions compared to those without among Patients with Systemic Lupus Erythematosus in the All of Us Research Program, v.7 (14,680 person-years) Conclusions In this large, diverse US-wide population of patients with SLE, we found that those with mental health diagnoses had higher rates of recurrent acute care use compared to those without these diagnoses. Mental health conditions may complicate the treatment and severity of SLE, leading to increased recurrent acute care use. As patients with frequent acute care use are less likely to receive standard-of-care long-term medications and preventive care, contributing to inequities, further research is needed to develop interventions to decrease this recurrent acute care use for patients with SLE and mental health disorders.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».